Hamilton Sundstrand Space Systems International, Inc.

Hamilton Sundstrand Corporation

Brian M Sutin

Cheng-Yi Lu

David Wait

Kenneth John Metcalf

Richard A Heppner

Rick L Howerton

Andrew Pargellis

Marcelo Bromberg

An imaging system comprises a wide field of view (FOV) telescope, a narrow FOV telescope, a spectrometer, an imaging detector, an image slicer, and a selection mechanism. The wide FOV telescope is configured to produce a one-dimensional optical image of a broad field of view (FOV) F. The narrow FOV telescope is configured to produce a two-dimensional optical image of a narrow FOV f. The spectrometer is configured to produce a spectrum of a one-dimensional image, and the imaging detector is configured to capture that spectrum. The image slicer is configured to break the two-dimensional optical image into a series of one-dimensional segments. The selection mechanism operable in either of two modes: a wide FOV mode transmitting the one-dimensional optical image to the spectrometer, and a zoom mode transmitting a one-dimensional concatenation of the series of one-dimensional segments to the spectrometer.

Imaging zoom for wide field-of-view imaging spectrometer

An ion trap comprises a ring electrode and opposite first and second endcap electrodes situated at opposite ends of the ring electrode. A waveform generator is configured to vary both frequency and amplitude of an AC waveform applied across the first and second endcap electrodes as a function of time, thereby exciting ions with a band of resonant secular frequencies substantially without exciting ions with adjacent secular frequencies.

Waveform generation for ion trap

A diagnostic device identifies failed sub-modules within a larger system based on error codes received from the system. The device stores a likelihood matrix that correlates each sub-module with each possible error code and maintains a likelihood value corresponding to the probability of a failed sub-module generating a corresponding error code and stores a prior probability of failure associated with each sub-module based on prior observational data. In response to received error codes, the device calculates a posterior probability of failure for each of the plurality of sub-modules based on a product of the likelihood values corresponding to the received error codes and the prior probability of failure associated with each sub-module. Based on the calculated posterior probability, the device identifies the sub-module with the highest posterior probability of failure as the failed sub-module.

Bayesian approach to identifying sub-module failure

An example space-based power generation panel arrangement includes a first reflective panel and at least one heat pipe configured to communicate thermal energy to the first reflective panel and a second reflective panel. The heat pipe is configured to hinge the first reflective panel to the second reflective panel.

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Claremont, CA, United States of America

Brian M Sutin